Machinelearning, abdsc, bigdata, datascience & much more…
AI News Tuesday, July 3
- Astonishing Hierarchy of Machine Learning Needs
MachineLearning, abdsc, BigData, DataScience, AI
- The AI bubble won’t burst anytime soon, but change is on the horizon
AI, BigData, IoT
- Artificial Intelligence (AI) in Retail Banking – Thematic Research
- Walking With AI: How to Spot, Store and Clean the Data You Need
- Curious to see our Swee app? – Enkronos – Medium
ai, ar, loyalty
- Chinese AI beats 15 doctors in tumor diagnosis competition
ai, china, medtech, HealthTech
- Event Handling in AWS using SNS, SQS, and Lambda
Event, SNS, machinelearning, SoftwareDevelopment, code, API
- AI’s impact on the future of work
- Apache Spark with Scala- Learning Path Decoded – Data Science Central
- Is AI set to transform the future of healthcare?
AI, healthcare, HealthTech
- Machine Learning is the hottest subject of todays time, DataScientist is the sexiest job of today but implementing these buzz words in real life business is most important need.
- The real need for todays time and business is to clarify, demonstrate and extract real values to benefit every one from this golden key word Machine Learning.
- Machine Learning is a focal point where business needs and experience (Mathematics, Statistics & Algorithmic logic/thinking) meet emerging technology and decides to work together to put useful results on table for real business.
- Machine learning helps data science by making a provision for data analysis, data preparation and even decision making.
- The wordlearninginmachine learningmeans that the algorithms depend on somedata, used as a training set, to fine-tune some model or algorithm parameters.
@KirkDBorne: The Astonishing Hierarchy of #MachineLearning Needs: https://t.co/V2FPdfEXUt #abdsc #BigData #DataScience #AI… https://t.co/Z34Q5Meo2G
- For starters, todays computer power has increased astronomically with GPUs (graphics processing unit, as opposed to CPU, central processing unit), and companies have created open source frameworks and made them public to developers over the past few years.
- In deep learning and machine learning, developers need to learn how to utilize new frameworks and it has less to do with classical programming and more to do with getting the different types of regressions and layers correct, and pasting components and tools together.
- Developers still need to have classical coding skills to develop products from these frameworks, which means you need to have teams of AI people and classical coders working in synchrony.
- Overcoming common misconceptions – – When you look at AI companies today, you automatically assume they should be founded by Ph.Ds.
- Well see a slew of new companies emerging in different fields of AI over the next several years.
@AllThings_IOT: The AI bubble won’t burst anytime soon, but change is on the horizon https://t.co/ittDlzGZTW #AI #BigData #IoT… https://t.co/XMlw9A9rKd
- NEW YORK, July 2, 2018 /PRNewswire/ — Artificial Intelligence (AI) in Retail Banking – Thematic Research – – Summary – – Read the full report: six decades machine learning (ML) was poised to take off because members of the ‘artificial intelligentsia’ had already come up with the theoretical models that…
- Scope – This report is part of our ecosystem of thematic investment research reports, supported by our thematic engine.
- About our Thematic Research Ecosystem – – – GlobalData has developed a unique thematic methodology for valuing technology, media and telecom companies based on their relative strength in the big investment themes that are impacting their industry.
- Whilst most investment research is underpinned by backwards looking company valuation models, GlobalData’s thematic methodology identifies which companies are best placed to succeed in a future filled with multiple disruptive threats.
- Our thematic research ecosystem has a three-tiered reporting structure: single theme, multi-theme and sector scorecard.
@IainLJBrown: Artificial Intelligence (AI) in Retail Banking – Thematic ResearchRead more here: https://t.co/SwxVIoZEb9… https://t.co/nKd7kHdUId
- To help entrepreneurs understand the importance of high-quality data, our team has come up with what we call the AI uncertainty principle: – – – – The key takeaway?
- We discussed evaluating business opportunities for AI in aprior Entrepreneur article, so we’re now focusing on the second variable: maximizing data quality.
- To get the good, clean data you need to: – – Machine learning initiatives are as diverse as companies themselves.
- Using UTM codes, the registry company had even gathered attribution data, something collected for all or most marketing activities by just51 percentof North American respondents to a 2017 AdRoll survey.
- If you roll out a new user interface, for example, clearly identify data from before and after the switch.
@4psa: Walking with #AI: How to spot, store and clean the data you need https://t.co/MU5WQEs60J https://t.co/KPA5hwZkaK
- Curious to see our Sweeapp?
- Curious to see our Swee app?
@enkronos: Curious to see our Swee app? You can watch a little video about it here: https://t.co/TsHVrp03Yr#ai #ar #loyalty… https://t.co/eGNBmQZy7X
@johnkoetsier: Chinese AI beats 15 doctors in tumor diagnosis competition#ai #china #medtech #HealthTech https://t.co/enDxebfBl8
@Lp1News: #Event Handling in AWS using #SNS, SQS, and Lambda https://t.co/htKeX3dGhV #machinelearning #SoftwareDevelopment #code #API
@sandrinea: AI’s impact on the future of work https://t.co/qfTr4SkLwj
@analyticbridge: Deep Learning Cheat Sheet (using Python Libraries) https://t.co/mvvDJTwsjg
@TamaraMcCleary: Is #AI set to transform the future of #healthcare? https://t.co/Nl1UnrqCZm #HealthTech https://t.co/RmwcFqA5O1
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